Articles

What Powers the Future of Artificial Intelligence

by Charlotte Lancaster We believe in Quality
Just a couple of years back, it might be hard to imagine just how important artificial intelligence would be for our everyday lives.

Nowadays, smart systems are powering world's biggest search engines, helping us sort never-ending heaps of data into meaningful categories, and will understand most of what we are saying and interpret into a different language.
This can be partially a natural consequence of the growth in computational power and greater availability of very capable hardware.


But hardware may not be the biggest driving force behind most recent artificial intelligence discoveries.

Our global transfer to the cloud has caused an unbelievable growth in regards to the amount of data saved online. This has a deep impact on the development
and the use of AI.

Modern Deep Learning networks can use collected information to learn and obtain the ability to, by way of example, identify spam email from authentic messages or arrange pictures of trees based on their species.

When taking a closer look at a few of the most significant subfields which are leading toward the progress of artificial intelligence by harnessing the power hidden inside large data collections, we can better understand where this intriguing technology going. Computers are obviously very good at resolving certain issues.

For example, even the least expensive computer that you can buy today could easily compute a complex trajectory of a moving thing, perform statistical analysis, or land a spacecraft on the Moon.

But there's another set of problems which is challenging to handle even to the most powerful supercomputers in life.

Contrary to the world of computers, the actual world is not predictable and algorithmic. In reality, it's fairly cluttered. That is why we have to heavily rely on instinct so as to recognize things, decide when we should visit a doctor, or what we ought to use when we go out. Machine learning has been already successfully used in practice to identify faces of people, localize earthquakes, predict changes on the stock exchange, or recommend users news topics based on their interests and preceding likes.

Machine learning could mostly be impossible, at least on the scale we see now if it wasn't for the usage of neural networks. They're approximations of the individual mind composed of hundreds and thousands of individual parts of hardware and software. Each little neuron is responsible for one, small task and its output gives the signal to higher systems.

A good case in point is a network designed to recognize handwriting. Their output is passed to other neurons, which function under a different set of rules until a result neuron is triggered.
The biggest drawback to neural networks is their reliance on large data collections and their slow learning speed.

Furthermore, their output is barely predictable, and it can take a very long time to detect the rationale behind a particular decision of a network. Integrative AI Similar to neurons in large neural networks, complex AI system necessitates integration of many competencies, such as eyesight, learning, language, language, preparation, and many others, allowing machines to fully act in an open-world environment.

Integrative AI would enable individuals to interact with machines on a more personal level, also it would allow machines to learn and retrieve new information in a more efficient method.

Regrettably, just a small progress was made in this region, and it will take several years of committed research prior to artificial intelligence systems are going to have the exact same perceptual ability as humans do.ConclusionDespite consumers getting gradually more used to the planet where smart systems are being able to do increasingly complex jobs, we still have a ways ahead of us before we could even remotely approach complex thinking of humans.

At precisely the same time, we have to thoroughly evaluate consequences arising from using artificial intelligence, as we proceed beyond Simple Neural Networks into programs which are more closely modeled on the individual neural arrangement.

These programs could very realistically start working in unpredictable ways which are beyond our immediate understanding.

However, all potential issues appear trivial, once we consider how functional AI could improve the standard of all facets of the life.

About Charlotte Lancaster Advanced   We believe in Quality

29 connections, 2 recommendations, 114 honor points.
Joined APSense since, May 31st, 2018, From Canada, Canada.

Created on Aug 3rd 2018 09:55. Viewed 352 times.

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